Apparatus and method for estimating biological component

ABSTRACT

An apparatus for non-invasively estimating a biological component is provided. The apparatus may include: a sensor configured to measure a calibration spectrum for a first duration and measure a biological component estimation spectrum for a second duration, based on light returning from an object; and a processor configured to remove a signal of a biological component from the calibration spectrum to obtain a background spectrum for the first duration, and estimate the biological component, based on the background spectrum and the biological component estimation spectrum, for the second duration in response to a command for measuring the biological component.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This is a continuation application of U.S. application Ser. No.15/810,916, filed Nov. 13, 2017, which claims priority from KoreanPatent Application No. 10-2016-0161724, filed on Nov. 30, 2016 in theKorean Intellectual Property Office, the disclosures of which areincorporated herein by reference in their entireties.

BACKGROUND 1. Field

Apparatuses and methods consistent with exemplary embodiments relate toestimating a biological component in a non-invasive manner.

2. Description of Related Art

Diabetes is a chronic disease that causes various complications and canbe hardly cured, and hence people with diabetes are advised to checktheir blood glucose regularly to prevent complications. In particular,when insulin is administered to control blood glucose, the blood glucoselevels may have to be closely monitored to avoid hypoglycemia andcontrol insulin dosage. There are several ways to monitor glucoselevels, from invasive finger pricking testing to non-invasive glucosemonitoring without causing pain. The invasive method may provide highreliability in measurement, but may cause pain and inconvenience as wellas an increased risk of disease infections due to the use of injection.Recently, extensive research has been conducted on non-invasivemeasurements of biological components, such as blood glucose, based onspectrum analysis without collecting blood.

SUMMARY

Exemplary embodiments address at least the above problems and/ordisadvantages and other disadvantages not described above. Also, theexemplary embodiments are not required to overcome the disadvantagesdescribed above, and may not overcome any of the problems describedabove.

According to an aspect of an exemplary embodiment, there is provided anapparatus for estimating a biological component including: a sensorconfigured to measure a calibration spectrum for a first duration andmeasure a biological component estimation spectrum for a secondduration, based on light returning from an object; and a processorconfigured to remove a signal of a biological component from thecalibration spectrum to obtain a background spectrum for the firstduration, and estimate the biological component, based on the backgroundspectrum and the biological component estimation spectrum, for thesecond duration in response to a command for measuring the biologicalcomponent.

The sensor may include: a light source configured to emit the light tothe object; and a detector configured to detect the light returning fromthe object and measure the calibration spectrum and the biologicalcomponent estimation spectrum based on the detected light.

The sensor may be further configured to measure the calibration spectrumand the biological component estimation spectrum based on at least oneof infrared spectroscopy and Raman spectroscopy.

The processor may include a calibrator configured to obtain thebackground spectrum by removing the signal of the biological componentfrom the calibration spectrum.

The calibrator may be further configured to remove the signal of thebiological component from the calibration spectrum based onLambert-Beer's law.

The calibrator may be further configured to generate the signal of thebiological component to obtain the background spectrum based on abiological component measurement value of the object received from anexternal device.

The biological component measurement value may be measured invasively bythe external device.

Two or more of the biological measurement values may be received fromthe external device.

The calibrator may be further configured to extract a background signalfrom the obtained background spectrum and calibrate a prediction modelbased on the extracted background signal.

The calibrator may be further configured to extract the backgroundsignal based on at least one of principal component analysis,independent component analysis, non-negative matrix factorization, andauto-encoding.

The processor may further include a component estimator configured toapply the prediction model to the measured biological componentestimation spectrum and estimate the biological component based on theprediction model.

The biological component may include at least one of blood glucose,cholesterol, neural fat, proteins, and uric acid.

The processor may include a quality manager configured to evaluate aquality of the measured biological component estimation spectrum anddetermine whether to estimate the biological component or recalibrate aprediction model according to the evaluated quality.

The quality manager may be further configured to evaluate the quality ofthe biological component estimation spectrum based on at least one ofnoise analysis and variation pattern analysis on the measured biologicalcomponent estimation spectrum.

According to an aspect of another exemplary embodiment, there isprovided a method of estimating a biological component, including:obtaining a calibration spectrum based on a first light returning froman object; removing a signal of a biological component from thecalibration spectrum to obtain a background spectrum; measuring abiological component estimation spectrum to estimate the biologicalcomponent based on a second light returning from the object in responseto a command for measuring the biological component; and estimating thebiological component based on the background spectrum and the biologicalcomponent estimation spectrum.

The method may further include: receiving a biological componentmeasurement value of the object from an external device; and generatingthe signal of the biological component for obtaining the backgroundspectrum based on the received biological component measurement value.

The biological component measurement value may be measured invasively bythe external device.

Two or more of the biological measurement values may be received fromthe external device.

The method may further include: extracting a background signal from theobtained background spectrum; and calibrating a prediction model basedon the extracted background signal.

The estimating the biological component may include applying themeasured biological component estimation spectrum to the predictionmodel to estimate the biological component.

The method may further include evaluating a quality of the measuredbiological component estimation spectrum and determining whether toestimate a biological component or recalibrate a prediction modelaccording to the evaluated quality.

The processor may be further configured to collect the backgroundspectrum as training data and calibrate a prediction model to estimatethe level of the biological component based on the collected trainingdata.

The processor may be further configured to collect a biologicalcomponent measurement value of the object from the object as thetraining data.

The processor may be further configured to generate the signal of thebiological component to obtain the background spectrum based on thebiological component measurement value among the training data andremove the generated biological component signal from the calibrationspectrum.

The processor may be further configured to extract a background signalfrom the background spectrum among the training data and calibrate theprediction model based on the extracted background signal.

The processor may be further configured to calibrate the predictionmodel using Lambert-Beer's law based on the background signal and a unitspectrum.

According to an aspect of another exemplary embodiment, there isprovided a wearable device including: a main body; a spectrometermounted in the main body and configured to measure a spectrum from anobject; and a processor mounted in the main body and configured tocalibrate a prediction model based on a background spectrum obtained byremoving a signal of a biological component from the spectrum measuredby the spectrometer and estimate the biological component based on thecalibrated prediction model.

The wearable device may further include a display mounted in the mainbody and configured to display the estimated biological component.

The wearable device may further include an operator mounted in the mainbody and configured to receive a calibration command or a biologicalcomponent estimation command from a user and transmit the receivedcommand to the processor.

The wearable device may further include a communicator mounted in themain body and configured to build a communication connection with anexternal device and receive a biological component measurement value ofthe object from the external device.

The biological component measurement value may be measured invasively bythe external device.

Two or more of the biological measurement values may be received fromthe external device.

According to an aspect of another exemplary embodiment, there isprovided a non-transitory computer readable storage medium storing aprogram that is executable by a computer to perform: obtaining acalibration spectrum based on a first light returning from an object;removing a signal of a biological component from the calibrationspectrum to obtain a background spectrum; measuring a biologicalcomponent estimation spectrum to estimate the biological component frombased on a second light returning from the object in response to acommand for measuring the biological component; and estimating thebiological component based on the background spectrum and the biologicalcomponent estimation spectrum.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects will be more apparent by describingcertain exemplary embodiments, with reference to the accompanyingdrawings, in which:

FIG. 1 is a block diagram illustrating an apparatus for estimatingbiological components according to an exemplary embodiment.

FIG. 2 is a block diagram of a measurer according to an exemplaryembodiment.

FIG. 3 is a block diagram illustrating a processor according to anexemplary embodiment.

FIG. 4 is a block diagram illustrating a processor according to anotherexemplary embodiment.

FIG. 5A is a diagram for describing a calibration method suitable for afasting period.

FIG. 5B is a diagram for describing a calibration method suitable bothfor fasting period and non-fasting period.

FIGS. 6A, 6B, 6C, and 6D are graphs for describing estimation of abiological component according to different calibration time points.

FIG. 7 is a block diagram illustrating an apparatus for estimating abiological component according to another exemplary embodiment.

FIG. 8 is a flowchart illustrating a method of estimating a biologicalcomponent according to an exemplary embodiment.

FIG. 9 is a flowchart illustrating a calibration method according to oneexemplary embodiment.

FIG. 10 is a flowchart illustrating a method of estimating a biologicalcomponent according to another exemplary embodiment.

FIG. 11 is a diagram illustrating a wearable device according to anexemplary embodiment.

FIG. 12 is a block diagram illustrating a configuration mounted in amain body of the wearable device of FIG. 11 .

DETAILED DESCRIPTION

Exemplary embodiments are described in greater detail below withreference to the accompanying drawings.

In the following description, like drawing reference numerals are usedfor like elements, even in different drawings. The matters defined inthe description, such as detailed construction and elements, areprovided to assist in a comprehensive understanding of the exemplaryembodiments. However, it is apparent that the exemplary embodiments canbe practiced without those specifically defined matters. Also,well-known functions or constructions are omitted to avoid obscuring thedescription with unnecessary detail.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. Also, the singular forms are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. In the specification, unless explicitly described to thecontrary, the words “comprise” and “include” and their variations suchas “comprises,” “comprising”, “includes,” or “including,” will beunderstood to imply the inclusion of stated elements but not theexclusion of any other elements. Terms such as “ . . . unit” and“module” denote units that process at least one function or operation,and they may be implemented by using hardware, software, or acombination of hardware and software.

Expressions such as “at least one of,” when preceding a list ofelements, modify the entire list of elements and do not modify theindividual elements of the list.

FIG. 1 is a block diagram illustrating an apparatus for estimatingbiological components according to an exemplary embodiment. Theapparatus 100 for estimating biological components may be mounted in awearable device which a user can wear. In this case, the wearable devicemay be of various types, such as a wrist watch type, a bracelet type, awristband type, a ring type, an eyeglass type, a hair band type, or thelike, and is not particularly limited in its shape, size, and the like.

Referring to FIG. 1 , the apparatus 100 includes a measurer or sensor110 and a processor 120.

The measurer 110 measures a spectrum of light reflected or scatteredfrom an object. The measurer 110 may be implemented with an opticalsensor. The object may be an upper part of a wrist where venous bloodvessels or capillaries are located, or a region of the wrist surfaceadjacent to the radial artery. In the case of a skin area where theradial artery passes, the influences of external factors, such as thethickness of inner skin tissue on the wrist, which may cause ameasurement error, may be relatively small. However, the object may notbe limited to the above examples, and may be a peripheral part of thehuman body, such as a finger, a toe, or an earlobe, where the bloodvessels are densely located.

In response to a specific control signal, the measurer 110 measures aspectrum by emitting light to the object and detecting light reflectedor scattered from the object. In this case, the measurer 110 may useinfrared spectroscopy or Raman spectroscopy, but is not limited thereto,and may measure the spectrum using various spectroscopy techniques.

FIG. 2 is a block diagram of a measurer according to an exemplaryembodiment. FIG. 2 illustrates one exemplary embodiment of the measurer110 of FIG. 1 . The measurer 200 may be implemented with a spectrometer,a laser-based fluorescence sensor, or a glucose-sensing contact lens.Referring to FIG. 2 , the measurer 200 includes a light source 210 and adetector 220.

The light source 210 may include a light emitting diode (LED), a laserdiode, or a fluorescent body, and may emit light of a near-infrared ray(NIR) band or a mid-infrared ray (MIR) band. In particular, the lightsource 210 may include an array of a plurality of light sources whichemit light of various wavelength bands in order to acquire accuratespectrum data. When the light source 210 emits light to the skin of auser, which is an object OBJ, in response to a control signal of theprocessor 120, the emitted light reaches a biological tissue, passingthrough the skin of the user. The light arriving at the biologicaltissue is scattered or reflected from the tissue and returns passingthrough the skin.

The detector 220 may detect the light returning from the skin of theuser and measure a spectrum of the detected light. In this case, thedetector 220 may be implemented with one or more InGaAs photodiodes, anda plurality of detectors may be provided.

The numbers and arrangements of the light sources 210 and the detectors220 may vary, and may be altered according to a type of biologicalcomponent, purpose of using the biological component, and the size andshape of a wearable device.

Referring back to FIG. 1 , the processor 120 processes various signalsand operations related to the estimation of biological component of theobject OBJ. Here, the biological component may include blood glucose,neural fat, cholesterol, proteins, and uric acid, but is not limitedthereto.

For example, the processor 120 may control the measurer 110 to measure aspectrum (hereinafter, will be referred to as a “biological componentestimation spectrum”) of light reflected or scattered from the objectOBJ for biological component estimation in response to a biologicalcomponent estimation command from the user. When the biologicalcomponent estimation spectrum is measured, the processor 120 maycalculate the biological component using the biological componentestimation spectrum and a background spectrum. To acquire the backgroundspectrum, a biological component signal may be removed from acalibration spectrum measured from the object OBJ.

In another example, upon a user's request for calibration, the processor120 may control the measurer 110 to measure a calibration spectrum atpredetermined intervals (e.g., 15 minutes) for a predetermined period oftime (e.g., 4 hours). When the calibration spectrum is measured, theprocessor 120 may perform a calibration operation, such as acquiring thebackground spectrum by removing a biological component signal from thecalibration spectrum.

FIG. 3 is a block diagram illustrating a processor according to oneexemplary embodiment. A configuration of the processor 300 in accordancewith one exemplary embodiment will be described in detail with referenceto FIGS. 1 and 3 .

Referring to FIG. 3 , the processor 300 includes a calibrator 310 and acomponent estimator 320.

The calibrator 310 may perform a calibration operation for biologicalcomponent estimation. For example, the calibrator 310 may control themeasurer 110 to acquire a calibration spectrum when a user requestscalibration or a preset standard is satisfied. The preset standard maybe a calibration spectrum measurement interval (e.g., days, weeks,months, or the like), and the measurement may be set to be performed ata specific time period during a pertinent interval. The specific timeperiod may be a fasting period, but is not limited thereto according tothe exemplary embodiment. The specific time period may be preset so thatthe measurement can be performed at an appropriate time in considerationof a life pattern of a user.

The calibrator 310 may receive a plurality of calibration spectra fromthe measurer 110 and acquire background spectra as training data. Forexample, the calibrator 310 may remove a biological component signalfrom each of the acquired calibration spectra and acquire the resultingspectra as the background spectra.

In addition, the calibrator 310 may collect biological componentmeasurement values measured by an external device as training data. Theexternal device may be a device that invasively measures a biologicalcomponent, but the type of the external device is not limited thereto.When a calibration operation is started upon the calibration request oraccording to the preset standard, the user may control the externaldevice to measure a biological component. For example, using theexternal device, the user may measure the biological component each timethe measurer 110 measures a calibration spectrum.

For instance, the calibrator 310 may control a communication module tobuild a communication connection with the external device, and mayreceive a measured biological component value when the external devicecompletes the measurement of the biological component. In this case, thecommunication module may be based on Bluetooth communication, Bluetoothlow energy (BLE) communication, near-field communication (NFC), wirelesslocal area network (WLAN) communication, ZigBee communication, infrareddata association (IrDA) communication, Wi-Fi direct (WFD) communication,ultra-wideband (UWB) communication, Ant+ communication, Wi-Ficommunication, radio frequency identification (RFID) communication, 3Gcommunication, 4G communication, 5G communication, but is not limitedthereto.

In another example, the calibrator 310 may receive the biologicalcomponent measurement value measured by the external device from theuser through an interface module. For example, the interface module mayinclude a display which allows text input or a microphone which allowsvoice input.

Meanwhile, when the calibrator 310 receives a biological componentmeasurement value that corresponds to each calibration spectrum, thecalibrator 310 may generate a biological component signal on the basisof the received biological component measurement value. For example, thecalibrator 310 may generate a biological component signal using thebiological component measurement value, a unit biological componentspectrum, and a light transmission path. In addition, when thebiological component signal is generated, the calibrator 310 may acquirethe background spectrum by removing the generated biological componentsignal from the calibration spectrum. In particular, the calibrator 310may remove the biological component signal from the calibration spectrumusing the Lambert-Beer's law.

When the background spectrum is acquired, the calibrator 310 may extracta background signal from the acquired background spectrum. In this case,the calibrator 310 may extract the background signal from the backgroundspectrum using principal component analysis (PCA), independent componentanalysis (ICA), non-negative matrix factorization, auto-encoding, andthe like.

When the background signal is extracted, the calibrator 310 may generatea prediction model for biological component estimation or calibrate apreviously generated prediction model using the extracted backgroundsignal, the unit biological component spectrum, and the lighttransmission path. The prediction model may be in the form of amathematical function expression based on the Lambert-Beer's law, but isnot limited thereto.

The component estimator 320 may estimate a biological component usingthe prediction model generated or calibrated by the calibrator 310. Whenthe measurer 110 measures a biological component estimation spectrumupon a user's request for biological component estimation, the componentestimator 320 may receive the biological component estimation spectrumfrom the measurer 110 and obtain a biological component of interest byapplying the prediction model to the received biological componentestimation spectrum.

FIG. 4 is a block diagram illustrating a processor according to anotherexemplary embodiment.

A configuration of a processor in accordance with another exemplaryembodiment will be described with reference to FIGS. 1 and 4 . Theprocessor 400 may further include a quality manager 410 in addition tothe calibrator 310 and the component estimator 320 of FIG. 3 . Thecalibrator 310 and the component estimator 320 are described withreference to FIG. 3 , and hence detailed description thereof will beomitted.

When the measurer 110 measures the biological component estimationspectrum upon a request for biological component estimation, the qualitymanager 410 may evaluate a quality of the measured biological componentestimation spectrum before transmitting it to the component estimator320. According to the evaluation result, the quality manager 410 mayallow the component estimator 320 to estimate a biological component orallow the measurer 110 to re-measure a biological component estimationspectrum. Alternatively, the quality manager 410 may allow thecalibrator 310 to re-perform the calibration operation on the predictionmodel.

When the calibrator 310 controls the measurer 110 to acquire a pluralityof calibration spectra in order to calibrate the prediction model, thequality manager 410 may analyze the plurality of acquired calibrationspectra to determine whether to continue to perform the calibrationoperation or re-perform the calibration operation at a different timeperiod.

For example, in order to determine whether to recalibrate the predictionmodel, the quality manager 410 may evaluate qualities of a plurality ofevaluation spectra. To obtain the evaluation spectra, the qualitymanager 410 may control the measurer 110 to measure the plurality ofevaluation spectra at predetermined intervals for a predetermined timeperiod either upon a user's request or at a preset interval. When theevaluation result shows that the qualities of the evaluation spectra donot satisfy a preset standard, the quality manager 410 may control thecalibrator 310 to recalibrate the prediction model.

When the biological component estimation spectrum or the plurality ofevaluation spectra are transmitted from the measurer 110, the qualitymanager 410 may evaluate the quality of the acquired spectrum usingnoise analysis or variation pattern analysis on the transmittedspectrum. For example, the quality manager 410 may calculate a noise ofthe acquired spectrum, and may perform recalibration when the noiseexceeds a specific baseline. In another example, the quality manager 410may convert the acquired time domain spectrum into a frequency domainspectrum by performing fast Fourier transform (TTF), and may determineto perform recalibration when a low-frequency random noise is detectedin the resultant frequency domain spectrum.

In yet another example, the quality manager 410 may calculate mutualsimilarities between a plurality of spectra, and determine whether toperform recalibration on the basis of the calculated similarities. Thequality manager 410 may determine to perform recalibration when astatistical value (e.g., an average, a maximum value, a minimum value, avariance, a standard deviation, etc.) of the calculated mutualsimilarities between the spectra is lower than a specific baseline.Alternatively, on the basis of the amount of change in the calculatedsimilarities, the quality manager 410 may determine whether eachspectrum has changed rapidly according to the elapse of measurementtime, and may determine to perform recalibration when the spectrum isdetermined to be changed abruptly. Specifically, the quality manager 410may compare a measured rate of the spectrum change to a predeterminedrate, and may determine to perform recalibration when the measured rateof the spectrum change is greater than the predetermined rate. Further,the similarities may be calculated using a similarity calculationalgorithm including Euclidean distance, Manhattan distance, cosinedistance, Mahalanobis distance, Jaccard coefficient, extended Jaccardcoefficient, Pearson's correlation coefficient, and Spearman'scorrelation coefficient.

Hereinafter, estimation of blood glucose, as an example of biologicalcomponents, will be described with reference to FIGS. 5A to 6D.

FIG. 5A is a diagram for describing a calibration method suitable for afasting period. FIG. 5B is a diagram for describing a calibration methodsuitable both for a fasting period and non-fasting period.

FIG. 5A shows that a blood glucose estimation apparatus obtains fivecalibration spectra S_(f)={S_(f1),S_(f2),S_(f3),S_(f4),S_(f5)} forcalibration at predetermined time intervals for a reference time periodCT. The reference time period CT corresponds to a fasting period, and itis assumed that a blood glucose level C₀ during the fasting period ismaintained substantially constant. Because C₀ is assumed constant, anexternal device may need to make its invasive measurement only onceduring the fasting period.

The blood glucose estimation apparatus may acquire background signalsBS={BS₁,BS₂,BS₃,BS₄,BS₅} from the obtained calibration spectra S_(f). Inthis case, the background signals may be obtained using a principalcomponent analysis technique. In addition, the number of obtainedbackground signals is not limited to five, and may be an arbitrarynumber. Once the background signals BS are obtained, a prediction modelis generated using the Lambert-Beer's law, as shown in Equation 1, andblood glucose may be estimated through Equation 2.S _(pt) =b ₁ BS ₁ +b ₂ BS ₂ +b ₃ BS ₃ +b ₄ BS ₄ +b ₅ BS ₅+ε_(g) L _(t)ΔC _(t)  (1)C _(t) =ΔC _(t) +C ₀  (2)

Here, S_(pt) denotes a skin spectrum measured at an actual measurementtime point t. ε_(g) denotes a spectrum of pure glucose of unitconcentration, and L_(t) denotes a light transmission path. Hereinafter,ε_(g) will be referred to as unit blood glucose spectrum. The unit bloodglucose spectrum ε_(g) and the light transmission path L_(t) are inputto the apparatus 100 in advance. ΔC_(t) denotes a difference betweenblood glucose C_(t) to be actually measured and the fasting bloodglucose C₀, b₁, b₂, b₃, b₄, b₅ denote coefficients of background signalsBS₁, BS₂, BS₃, BS₄, BS₅, respectively, which are calculated using aleast square method or the like.

In other words, when a skin spectrum is measured at the actual bloodglucose measurement time point t, the blood glucose estimation apparatusmay apply the skin spectrum to Equation 1 to estimate a blood glucosesignal ε_(g)L_(t)ΔC_(t), and may calculate the amount of change in bloodglucose at the time point t by using the unit blood glucose spectrum andthe light transmission path L_(t). Because, it is assumed that thefasting blood glucose is maintained constant, so when the amount ofchange in blood glucose ΔC_(t) is calculated, it is possible to estimatethe blood glucose C_(t) at the time point t by applying the calculatedamount of change in blood glucose ΔC_(t) to Equation 2.

While, it is assumed that a baseline blood glucose level for calibrationis constant during a fasting period, it is difficult to obtain a periodof time during which the blood glucose level is actually maintainedconstant. In addition, a fasting period occurs mostly at dawn so that itis inconvenient to measure a spectrum. Furthermore, if blood glucoselevels fluctuate even with fasting, it is difficult to accuratelycalibrate the prediction model. For example, in the case of diabeticpatients who require accurate blood glucose estimation, the fastingblood glucose level is often not maintained constant. Therefore, thediabetic patients may need a blood glucose estimation method that doesnot assume a constant blood glucose level during a fasting period.

Estimation of blood glucose regardless of fasting will be described withreference to FIGS. 1 and 5B.

FIG. 5B illustrates five calibration spectraS_(c)={S_(c1),S_(c2),S_(c3),S_(c4),S_(c5)} obtained in a reference stateCT for calibration, but the number of calibration spectra is not limitedthereto. The reference state CT for calibration may or may not be afasting period. The blood glucose in the reference state CT is notassumed to be constant, as described below.

When the calibration spectra S_(c) are obtained, the apparatus 100 mayacquire background spectra S_(b)={S_(b1),S_(b2),S_(b3),S_(b4),S_(b5)} byeliminating a blood glucose signal ε_(g)L_(t)C_(m) from the calibrationspectra S_(c), as shown in the following Equation 3.S _(b) =S _(c)−ε_(g) L _(t) C _(m)  (3)

Here, ε_(g) denotes a unit blood glucose spectrum, and L_(t) denotes alight transmission path. The unit blood glucose spectrum and the lighttransmission path are values which are input in advance. C_(m) denotes areal blood glucose value measured invasively by an external device. Inother words, an external device may need to make invasive measurementstwo times or more for calibration. The real blood glucose value may bemeasured at the time of measuring a calibration.

Then, the apparatus 100 may acquire background signalsBS={BS₁,BS₂,BS₃,BS₄,BS₅} from the background spectra S_(b) obtainedusing a principal component analysis. When the background signals BS areobtained, the apparatus 100 may generate a prediction model using theLambert-Beer's law, as shown in Equation 4. When a spectrum S_(pt) ismeasured at time t at which a real blood glucose is to be measured, theapparatus 100 may estimate the blood glucose signal ε_(g)L_(t)C_(t) byapplying the prediction model to the spectrum S_(pt). When the bloodglucose signal ε_(g)L_(t)C_(t) is obtained as described above, a desiredblood glucose value C_(t) may be estimated based on the unit bloodglucose spectrum and the light transmission path. b₁, b₂, b₃, b₄, b₅denote coefficients of the background signals BS₁, BS₂, BS₃, BS₄, andBS₅, respectively, which are calculated using a least square method.S _(pt) =b ₁ BS ₁ +b ₂ BS ₂ +b ₃ BS ₃ +b ₄ BS ₄ +b ₅ BS ₅+ε_(g) L _(t) C_(t)  (4)

According to the embodiment, a constant baseline blood glucose level isnot required, so that it is possible to perform calibration without timeconstraints.

FIGS. 6A to 6D are graphs for describing estimation of a biologicalcomponent according to different calibration time points.

FIG. 6A shows result of estimation of a blood glucose level assuming aconstant blood glucose level during the fasting time period CT. FIG. 6Bshow the result when no such assumption was made. In the case of FIG.6A, a correlation R between the actual measurement value and aprediction value is 0.893, while in the case of FIG. 6B, the Pearsoncorrelation coefficient R is 0.885. Therefore, it is seen that bothmethods result in accurate blood glucose measurement.

FIGS. 6C and 6D show results of estimation of a blood glucose level in atime period during which the blood glucose level is not maintainedconstant. In the case of FIG. 6C where a constant blood glucose level isassumed for the calibration period CT, a correlation R between theactual measurement value and a prediction value is 0.608, while in thecase of FIG. 6D where no such assumption is made, the Pearsoncorrelation coefficient R is 0.728. Thus, it is seen that the bloodglucose measurement result making no constant blood glucose level ismore accurate.

FIG. 7 is a block diagram illustrating an apparatus for estimating abiological component according to another exemplary embodiment.

Referring to FIG. 7 , an apparatus 700 for estimating a biologicalcomponent includes a measurer 110, a processor 120, an interface 710,and a communicator 720. The configurations of the measurer 110 and theprocessor 120 are described in detail with reference to FIG. 1 and thefollowing drawings, and hence the description will be made in focus withthe interface 710 and the communicator 720.

The interface 710 interacts with a user using an interface module. Forexample, the interface module may include a display, a speaker, amicrophone, a haptic device, and the like, but is not limited thereto.

The interface 710 may provide a user interface that allows a user toinput a command related to biometric component estimation. For example,the interface 710 may provide a user interface that guides the user toinput a command through a touch input to a display or through a separateoperation module. In addition, in the case in which a wearable deviceincludes voice recognition features, the interface 710 may output guideinformation through a speaker so that the user can input a command byvoice.

The interface 710 may transmit the command input from the user to theprocessor 120 and provide a processing result from the processor 120 tothe user through the user interface. In this case, the interface 710 mayprovide biological component information or warning or alarm informationusing a visual method, such as an output color, or a non-visual method,such as a vibration, a tactile sensation, voice, or the like. Forexample, a level of the blood glucose value and an output method foreach level may be set in advance according to the user'scharacteristics, such as a user's health status. When the blood glucosevalue is estimated by the processor 120, the interface 710 may providethe pertinent information to the user using an output methodcorresponding to the level in which the estimated blood glucose value ispresent.

The communicator 720 may access a wireless communication network underthe control of the processor 120 and may build a communicationconnection with an external device. In this case, the external devicemay include various information processing devices, such as asmartphone, a tablet PC, a desktop PC, and a notebook PC, in addition toa device that invasively measures a biological component, as describedabove.

The processor 120 may receive a biological component value measured bythe external device. In addition, the processor 120 may transmitspectrum data measured by the measurer 111 or data related to theestimated biological component to the external device, and the externaldevice may use the received data in monitoring of the user's healthstatus. For instance, the external device may manage a history ofchanges in the biological component, e.g., a blood glucose level, andprovide relevant information to the user in various ways, such as agraph.

FIG. 8 is a flowchart illustrating a method of estimating a biologicalcomponent according to one exemplary embodiment.

The method illustrated in FIG. 8 may be one exemplary embodimentperformed by the apparatus 100 for estimating a biological componentshown in FIG. 1 . As described above in detail, the method will bedescribed in brief to avoid redundancy.

Referring to FIG. 8 , the apparatus 100 measures a biological componentestimation spectrum from a user's skin upon a user's request, inoperation 810.

In operation 820, the apparatus 100 removes a biological componentsignal from a calibration spectrum to obtain a background spectrum, andmeasures the biological component based on the background spectrum. Forexample, when the biological component estimation spectrum is measured,the apparatus 100 may estimate the biological component using aprediction model which has been calibrated through calibration process.The prediction model may be calibrated based on the background spectrumobtained from a calibration spectrum measured as described withreference to FIG. 9 .

Thereafter, the apparatus 100 provides a variety of informationincluding a biological component estimation result to the user, inoperation 830.

FIG. 9 is a flowchart illustrating a calibration method according to oneexemplary embodiment.

The method illustrated in FIG. 9 is one exemplary embodiment of acalibration method performed by the apparatus 100 of FIG. 1 .

When the apparatus 100 for estimating a biological component receives acalibration command in operation 910, the apparatus 100 controls a lightsource and a detector to measure a calibration spectrum from an object,in operation 920. The calibration command may be input by a user, but isnot limited thereto. A calibration interval may be set in advanceaccording to necessity. A plurality of calibration spectra may bemeasured at predetermined intervals for a predetermined time period.

When the calibration command is received in operation 910, the apparatus100 receives biological component measurement values from an externaldevice and may collect the received biological component measurementvalues as training data, in operation 960. The external device may be adevice that invasively measures a biological component, and theapparatus 100 may be connected to the external device using a mountedcommunication module. Alternatively, the apparatus 100 may provide aninterface to the user to receive the biological component measurementvalue through an interface module and may receive the biologicalcomponent measurement values measured by the external device from theuser. The external device may measure the biological component valueeach time the apparatus 100 acquires a calibration spectrum.

Thereafter, the apparatus 100 generates a biological component signalusing the biological component measurement value measured by theexternal device in operation 930. At this time, the biological componentsignal may be generated using the received biological componentmeasurement value and the unit biological component spectrum and thelight transmission path which have been input to the apparatus 100 inadvance.

In operation 940, the apparatus 100 acquires a background spectrum byeliminating the biological component signal from the measuredcalibration spectrum and collects the acquired background spectrum astraining data. For example, the apparatus 100 may remove the biologicalcomponent signal from the calibration spectrum using the Lambert-Beer'slaw.

In operation 950, the apparatus 100 generates or calibrates a predictionmodel for biological component estimation on the basis of the acquiredbackground spectrum. For example, the apparatus 100 may acquire abackground signal from the background spectrum using principal componentanalysis and calibrate the prediction model into the form of amathematical expression on the basis of the acquired background signal,using the Lambert-Beer's law.

FIG. 10 is a flowchart illustrating a method of estimating a biologicalcomponent according to another exemplary embodiment.

The method illustrated in FIG. 10 may be another embodiment of themethod of estimating a biological component performed by the apparatus100 of FIG. 1 .

Referring to FIG. 10 , the apparatus 100 for estimating a biologicalcomponent receives a command from a user, as depicted in 1010, anddetermines whether the received command is a command for estimating abiological component or a calibration command, as depicted in 1020.

If the command is for estimating a biological component, the apparatus100 emits light to the user's skin, by driving a light source andmeasures a biological component estimation spectrum on the basis of alight signal detected by a detector, in operation 1030.

Then, the apparatus 100 evaluates a quality of the measured biologicalcomponent estimation spectrum and determines whether the biologicalcomponent estimation spectrum is suitable for estimating a biologicalcomponent, in operation 1040. For example, the apparatus 100 maycalculate a noise of the biological component measurement spectrum anddetermine whether the quality of the biological component measurementspectrum is satisfactory or not according to whether or not the noiseexceeds a predetermined standard. Alternatively, the apparatus 100 maycalculate similarities between a plurality of biological componentestimation spectra measured for a predetermined time period, identifythe variation pattern of each spectrum over time on the basis of thesimilarities, and determine that a spectrum which has a change rategreater than a threshold change rate is not suitable for estimating abiological component. However, the exemplary embodiments are not limitedto the above examples.

Then, when it is determined that the biological component estimationspectrum is suitable for estimating a biological component, theapparatus 100 measures a biological component by applying a predictionmodel to the biological component estimation spectrum, which has beencalibrated in advance, in operation 1050.

The, the apparatus 100 provides a biological component estimation resultto the user, in operation 1060. The biological component estimationresult may be displayed visually using colors, different linethicknesses, or various graphs. Alternatively, the biological componentestimation result may be provided using a non-visual method, such as avoice, a tactile sensation, or a vibration.

If it is determined in operation 1040 that the quality of the biologicalcomponent estimation spectrum measured is not suitable for estimating abiological component, the apparatus 100 may recalibrate the predictionmodel, as described above in reference with FIG. 9 . In addition, whenit is determined in operation 1020 that the user's command is acalibration command, the apparatus 100 recalibrates the prediction modelin operation 1080.

FIG. 11 is a diagram illustrating a wearable device according to oneexemplary embodiment. FIG. 12 is a block diagram illustrating aconfiguration mounted in a main body of the wearable device of FIG. 11 .

As illustrated in FIGS. 11 and 12 , various exemplary embodiments of theapparatus for estimating a biological component may be mounted in asmart band-type wearable device. However, it is purely one example forconvenience of description, and it should not be construed that theexemplary embodiments are applied only to the smart band-type wearabledevice.

Referring to FIGS. 11 and 12 , the wearable device 1100 includes a mainbody 1110 and a strap including strap members 1113 and 1114.

The strap may be formed to be flexible and be bent in such a manner thateach strap member can be wrapped around and removed from a user's wrist.In this case, a battery for supplying power to the wearable device 100may be embedded in the main body 1110 or the strap member 1114.

The main body 1110 of the wearable device 1100 may include aspectrometer 1210 and a processor 1220. The spectrometer emits light tothe user's skin and measures a spectrum by dispersing the lightscattered or reflected from the skin, and the processor 1220 estimates auser's biological component using the spectrum measured by thespectrometer 1210.

In response to a control signal, the processor 1220 drives thespectrometer 1210 to emit light to the skin of the user's wrist area anddetects light returning from the skin. The light emitted from a lightsource reaches a biological tissue, passing through the user's skin, andthe light arriving at the biological tissue reacts with the biologicaltissue and returns. The spectrometer 1210 obtains a spectrum of thereturning light and transmits the spectrum to the processor 1220. Inthis case, the light source may be configured to emit light of an NIRband or an MIR band.

In addition, the spectrometer 1210 may include a linear variable filter(LVF). The LVF has a spectral characteristic that linearly changes overthe entire length. Therefore, the LVF may disperse incident light inwavelength order. The LVF has a compact size and an excellent spectralperformance.

The processor 1220 may process various commands once the commands areinput through an operator 1112 or a display 1111. For example, when acalibration command is input by the user, calibration of a predictionmodel may be performed, as described above. In addition, when abiological component estimation command is input by the user, abiological component may be estimated based on the calibrated predictionmodel. In particular, the processor 1220 may evaluate the quality of anoptical spectrum. When the quality of the measured spectrum is notsuitable for estimating a biological component, calibration may bere-performed. In addition, the processor 1220 may generate a variety ofhealthcare information, such as warnings, alarms, and a history ofchanges in health status, on the basis of the estimated biologicalcomponent information.

The operator 1112 mounted in the main body 1110 may receive a user'scontrol command and transmit it to the processor 1220, and may include apower button for inputting a command for power on/off of the wearabledevice 1100.

The display 1111 mounted in the main body 1110 may provide a variety ofinformation generated by the processor 1220, for example, informationrelated to a biological component and other various types of information(e.g., time, weather, etc.) to the user under the control of theprocessor 1220.

The communicator 1230 may communicate with the external device under thecontrol of the processor 1220. The external device may be an informationprocessing device, such as a smartphone, a desktop computer, a notebookcomputer, or the like, which has superior computing performance. Inaddition, the external device may be an invasive biological componentmeasurement device.

While not restricted thereto, an exemplary embodiment can be embodied ascomputer-readable code on a computer-readable recording medium. Thecomputer-readable recording medium is any data storage device that canstore data that can be thereafter read by a computer system. Examples ofthe computer-readable recording medium include read-only memory (ROM),random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, andoptical data storage devices. The computer-readable recording medium canalso be distributed over network-coupled computer systems so that thecomputer-readable code is stored and executed in a distributed fashion.Also, an exemplary embodiment may be written as a computer programtransmitted over a computer-readable transmission medium, such as acarrier wave, and received and implemented in general-use orspecial-purpose digital computers that execute the programs. Moreover,it is understood that in exemplary embodiments, one or more units of theabove-described apparatuses and devices can include circuitry, aprocessor, a microprocessor, etc., and may execute a computer programstored in a computer-readable medium.

The foregoing exemplary embodiments are merely exemplary and are not tobe construed as limiting. The present teaching can be readily applied toother types of apparatuses. Also, the description of the exemplaryembodiments is intended to be illustrative, and not to limit the scopeof the claims, and many alternatives, modifications, and variations willbe apparent to those skilled in the art.

What is claimed is:
 1. A wearable device comprising: a main body; aspectrometer mounted in the main body and configured to measure aspectrum from an object; a processor mounted in the main body andconfigured to: generate a signal of a biological component by using aunit spectrum of the biological component and at least one biologicalcomponent measurement value; calibrate a prediction model based on abackground spectrum obtained by removing the signal of the biologicalcomponent from the spectrum measured by the spectrometer; and estimatethe biological component based on the calibrated prediction model, adisplay mounted in the main body and configured to display the estimatedbiological component; an operator mounted in the main body andconfigured to receive a calibration command or a biological componentestimation including blood glucose command from a user and transmit thereceived command to the processor; and a communicator mounted in themain body and configured to establish a communication connection with anexternal device and receive a biological component measurement valueincluding blood glucose of the object from the external device.
 2. Thewearable device of claim 1, wherein the spectrometer is configured tomeasure the spectrum based on at least one of infrared spectroscopy andRaman spectroscopy.
 3. The wearable device of claim 1, wherein theprocessor is configured to remove the signal of the biological componentfrom the spectrum based on Lambert-Beer's law.
 4. The wearable device ofclaim 1, wherein the processor is configured to extract a backgroundsignal from the obtained background spectrum and calibrate theprediction model based on the extracted background signal.
 5. Thewearable device of claim 4, wherein the processor is further configuredto extract the background signal based on at least one of principalcomponent analysis, independent component analysis, non-negative matrixfactorization, and auto-encoding.
 6. A wearable device comprising: amain body; a spectrometer mounted in the main body and configured tomeasure a spectrum from an object; a processor mounted in the main bodyand configured to: generate a signal of a biological component by usinga unit spectrum of the biological component and at least one biologicalcomponent measurement value; calibrate a prediction model based on abackground spectrum obtained by removing the signal of the biologicalcomponent from the spectrum measured by the spectrometer; and estimatethe biological component based on the calibrated prediction model, adisplay mounted in the main body and configured to display the estimatedbiological component; an operator mounted in the main body andconfigured to receive a calibration command or a biological componentestimation command from a user and transmit the received command to theprocessor; and a communicator mounted in the main body and configured toestablish a communication connection with an external device and receivea biological component measurement value of the object from the externaldevice.
 7. The wearable device of claim 6, wherein the biologicalcomponent comprises at least one of blood glucose, cholesterol, neuralfat, proteins, and uric acid.